Buch, Englisch, 301 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 482 g
Reihe: Recent Advancements in Connected Autonomous Vehicle Technologies
Buch, Englisch, 301 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 482 g
Reihe: Recent Advancements in Connected Autonomous Vehicle Technologies
ISBN: 978-981-99-7792-5
Verlag: Springer Nature Singapore
- Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods.
- Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers.
- Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account.
Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. - Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1. Background.- Chapter 2. Robust Environmental Perception of Multi-Sensor Data Fusion.- Chapter 3. Robust Environmental Perception of Monocular 3D Object Detection.- Chapter 4. Robust Environmental Perception of Semantic Segmentation.- Chapter 5. Robust Environmental Perception of Trajectory Prediction.- Chapter 6 Robust Environmental Perception of Multi-object Tracking.- Chapter 7. Reliability Control of Intelligent Vehicles.- References.